How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

Is the economic uncertainty- human health relationship nonlinear? An empirical analysis for the China

The health costs of economic uncertainty always remain a major concern among policymakers of China. The theoretical and empirical literature on the economic uncertainty-human health nexus is still in its infancy stage. This study is firmly rooted in the economic uncertainty theory advanced by Baker, Bloom, & Davis. In this study, the primary objective of the analysis is to estimate the asymmetric impact of economic uncertainty on human health in China's economy. In order to evaluate the short and long-run estimates of economic uncertainty on human health across various quantiles, we have employed the linear and nonlinear QARDL models. The linear QARDL model shows that the long-run relationship between economic uncertainty and the infant mortality rate is positive and significant at all quantiles, while the long-run relationship between economic uncertainty and the death rate is positive and significant at higher quantiles. The nonlinear QARDL model reveals that, in the long run, the relationship between the positive shock of economic uncertainty and the infant mortality rate is positive and significant at quantiles 0.30 to 0.95, while the long-run relationship between the positive shock of economic uncertainty and the death rate is positive and significant at higher quantiles. The relationship between the negative shock of economic uncertainty and the infant mortality rate is negative and significant at the highest quantiles, while the relationship between the negative shock of economic uncertainty and death rate is negative and significant at higher quantiles in the long run. The findings indicate a positive relationship between economic uncertainty in China and higher rates of infant mortality and death. Thus, adopting suitable policies for controlling economic uncertainty can help in improving human health in China.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Discrete Event Simulation Model for Cost-Effectiveness Evaluation of Screening for Asymptomatic Patients with Lower Extremity Arterial Disease

Lower limb ischemic disease (LEAD) affects a significant portion of the population, with most patients being asymptomatic. Patient screening is necessary because LEAD patients have an increased risk of occurrence of other cardiovascular events and manifestations of disease, in terms of leg symptoms such as intermittent claudication, critical limb ischemia, or amputation. The aim of this work was to evaluate the cost-effectiveness of screening using ABI diagnostics in asymptomatic patients and its impact on limb symptoms associated with LEAD. A discrete event simulation model was created to capture lifetime costs and effects. Costs were calculated from the perspective of the health care payer, and the effects were calculated as QALYs. A cost-effectiveness analysis was performed to compare ABI screening examination and the situation without such screening. A probabilistic sensitivity analysis and scenario analysis were carried out to evaluate the robustness of the results. In the basic setting, the screening intervention was a more expensive intervention, at a cost of CZK 174,010, compared to CZK 70,177 for the strategy without screening. The benefits of screening were estimated at 14.73 QALYs, with 14.46 QALYs without screening. The final ICER value of CZK 389,738 per QALY is below the willingness to pay threshold. Likewise, the results of the probabilistic sensitivity analysis and of the scenario analysis were below the threshold of willingness to pay, thus confirming the robustness of the results. In conclusion, ABI screening appears to be a cost-effective strategy for asymptomatic patients aged 50 years when compared to the no-screening option.

A scoping review on patient heterogeneity in economic evaluations of precision medicine based on basket trials

INTRODUCTION

Considerable challenges in the economic evaluation of precision medicines have been mentioned in previous studies. However, they have not addressed how an economic assessment would be conducted based on basket trials (novel studies for evaluation of precision medicine effects) in which the included populations have specific biomarkers and various cancers. Since basket trial populations have remarkable heterogeneity, this study aims to investigate the concept of heterogeneity and specific method(s) for considering it in economic evaluations through guidelines and studies that could be applicable in economic evaluation based on basket trials.

AREA COVERED

We searched PubMed, Web of Science, Scopus, Google Scholar, and Google to find studies and pharmacoeconomics guidelines. The inclusion criteria included subjects of patient heterogeneity and suggested explicit method(s). Thirty-nine guidelines and 43 studies were included and evaluated. None of these materials mentioned disease types in a target population as a factor causing heterogeneity. Moreover, in economic evaluations, patient heterogeneity has been considered with four general approaches subgroup analysis, individual-based models, sensitivity analysis, and regression models.

EXPERT OPINION

Type of disease is not considered a contributing factor in population heterogeneity, and the probable appropriate method for this issue could be individual-based models.

Calibrating Natural History of Cancer Models in the Presence of Data Incompatibility: Problems and Solutions

The calibration of cancer natural history models is often challenged by a lack of representative calibration targets, forcing modellers to rely on potentially incompatible datasets. Using a microsimulation colorectal cancer model as an example, the purposes of this paper are to (1) highlight the reasons for uncertainty in calibration targets, (2) illustrate practical and generalisable approaches for dealing with incompatibility in calibration targets, and (3) discuss the importance of future research in the area of incorporating uncertainty in calibration. The low quality of data and differences in populations, outcome definitions, and healthcare systems may result in incompatibility between the model and the data. Acknowledging reasons for data incompatibility allows assessment of the risk of incompatibility before calibrating the model. Only a few approaches are available to address data incompatibility, for instance addressing biases in calibration targets and their adjustment, relaxing the goodness-of-fit metric, and validation of the calibration targets to the data not used in the calibration. However, these approaches lack explicit comparison and validation, and so more research is needed to describe the nature and causes of indirect uncertainty (i.e. uncertainty that cannot be expressed in absolute quantitative forms) and identify methods for managing this uncertainty in healthcare modelling.

Four Aspects Affecting Health Economic Decision Models and Their Validation

Health care decision makers in many jurisdictions use cost-effectiveness analysis based on health economic decision models for policy decisions regarding coverage and price negotiation for medicines and medical devices. While validation of health economic decision models has always been considered important, many reviews of model-based cost-effectiveness studies report limitations regarding their validation. The current opinion paper discusses four aspects of current health economic decision modeling with relevance for future directions in model validation: increased use of complex models, international cooperation, open-source modeling, and stakeholder involvement. First, new, more complex clinical study designs and treatment strategies may require relatively complex model structures and/or input data analyses. Simultaneously, more widespread technical knowledge along with wider data availability have led to a broader range of model types. This puts extra requirements on model validation and transparency. Second, increased international cooperation of policy makers and, in particular, health technology assessment (HTA) authorities in performing model assessments is discussed in relation to the repeated use of health economic models (multi-use disease models). We argue such coordinated efforts may benefit model validity. Third, open-source modeling is discussed as one possible answer to increased transparency requirements. Finally, involvement of all relevant stakeholders throughout the whole decision process is an ongoing development that necessarily also includes health economic modeling. We argue this implies that model validity should be considered in a broader perspective, with more focus on conceptual modeling, model transparency, accuracy requirements, and choice of relevant model outcomes than previously.

Cost-effectiveness of the fixed-dose combination tiotropium/olodaterol versus tiotropium monotherapy or a fixed-dose combination of long-acting β2-agonist/inhaled corticosteroid for COPD in Finland, Sweden and the Netherlands: a model-based study

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) guidelines advocate treatment with combinations of long-acting bronchodilators for patients with COPD who have persistent symptoms or continue to have exacerbations while using a single bronchodilator. This study assessed the cost-utility of the fixed dose combination of the bronchodilators tiotropium and olodaterol versus two comparators, tiotropium monotherapy and long-acting β2 agonist/inhaled corticosteroid (LABA/ICS) combinations, in three European countries: Finland, Sweden and the Netherlands.

METHODS

A previously published COPD patient-level discrete event simulation model was updated with most recent evidence to estimate lifetime quality-adjusted life years (QALYs) and costs for COPD patients receiving either tiotropium/olodaterol, tiotropium monotherapy or LABA/ICS. Treatment efficacy covered impact on trough forced expiratory volume in 1 s (FEV1), total and severe exacerbations and pneumonias. The unit costs of medication, maintenance treatment, exacerbations and pneumonias were obtained for each country. The country-specific analyses adhered to the Finnish, Swedish and Dutch pharmacoeconomic guidelines, respectively.

RESULTS

Treatment with tiotropium/olodaterol gained QALYs ranging from 0.09 (Finland and Sweden) to 0.11 (the Netherlands) versus tiotropium and 0.23 (Finland and Sweden) to 0.28 (the Netherlands) versus LABA/ICS. The Finnish payer's incremental cost-effectiveness ratio (ICER) of tiotropium/olodaterol was €11 000/QALY versus tiotropium and dominant versus LABA/ICS. The Swedish ICERs were €6200/QALY and dominant, respectively (societal perspective). The Dutch ICERs were €14 400 and €9200, respectively (societal perspective). The probability that tiotropium/olodaterol was cost-effective compared with tiotropium at the country-specific (unofficial) threshold values for the maximum willingness to pay for a QALY was 84% for Finland, 98% for Sweden and 99% for the Netherlands. Compared with LABA/ICS, this probability was 100% for all three countries.

CONCLUSIONS

Based on the simulations, tiotropium/olodaterol is a cost-effective treatment option versus tiotropium or LABA/ICS in all three countries. In both Finland and Sweden, tiotropium/olodaterol is more effective and cost saving (ie, dominant) in comparison with LABA/ICS.

Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine

OBJECTIVE

The objective of this study was to develop guidance contributing to improved consistency and quality in economic evaluations of personalised medicine (PM), given current ambiguity about how to measure the value of PM as well as considerable variation in the methodology and reporting in economic evaluations of PM.

METHODS

A targeted literature review of methodological papers was performed for an overview of modelling challenges in PM. Expert interviews were held to discuss best modelling practice. A systematic literature review of economic evaluations of PM was conducted to gain insight into current modelling practice. The findings were synthesised and used to develop a set of draft recommendations. The draft recommendations were discussed at a stakeholder workshop and subsequently finalised.

RESULTS

Twenty-two methodological papers were identified. Some argued that the challenges in modelling PM can be addressed within existing methodological frameworks, others disagreed. Eighteen experts were interviewed. They believed large uncertainty to be a key concern. Out of 195 economic evaluations of PM identified, 56% addressed none of the identified modelling challenges. A set of 23 recommendations was developed. Eight recommendations focus on the modelling of test-treatment pathways. The use of non-randomised controlled trial data is discouraged but several recommendations are provided in case randomised controlled trial data are unavailable. The parameterisation of structural uncertainty is recommended. Other recommendations consider perspective and discounting; premature survival data; additional value elements; patient and clinician compliance; and managed entry agreements.

CONCLUSIONS

This study provides a comprehensive list of recommendations to modellers of PM and to evaluators and reviewers of PM models.

Home telemonitoring and a diagnostic algorithm in the management of heart failure in the Netherlands: a cost-effectiveness analysis (Preprint)

Background

Objective

Methods

Results

Conclusions